Instructions to use trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
model = AutoModelForCausalLM.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/trl-internal-testing/tiny-Qwen2ForCausalLM-2.5

SGLang

How to use trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 with Docker Model Runner:
```
docker model run hf.co/trl-internal-testing/tiny-Qwen2ForCausalLM-2.5
```

Upload README.md with huggingface_hub

by Jiaao - opened May 22

base: refs/heads/main

←

from: refs/pr/3

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README.md +38 -4

README.md CHANGED Viewed

@@ -1,9 +1,43 @@
 ---
-library_name: transformers
 tags:
-- trl
 ---
-# Tiny Qwen2ForCausalLM
-This is a minimal model built for unit tests in the [TRL](https://github.com/huggingface/trl) library.

 ---
+language:
+- en
+license: apache-2.0
 tags:
+- text-generation
+- transformers
+pipeline_tag: text-generation
 ---
+# tiny-Qwen2ForCausalLM-2.5
+## Description
+This is a tiny test model based on the Qwen2 architecture for text-generation tasks. It is designed for testing and development purposes.
+## Intended use
+This model is intended for:
+- Testing and development of text generation pipelines
+- Research and experimentation with transformer models
+- Educational purposes
+## Limitations
+This is a tiny test model with limited capabilities:
+- Not suitable for production use
+- Limited vocabulary and context length
+- May produce nonsensical outputs
+## How to use
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
+tokenizer = AutoTokenizer.from_pretrained("trl-internal-testing/tiny-Qwen2ForCausalLM-2.5")
+inputs = tokenizer("Hello, how are you?", return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=50)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```